import torch


def to_numpy(tensor):
    if torch.is_tensor(tensor):
        return tensor.cpu().numpy()
    elif type(tensor).__module__ != 'numpy':
        raise ValueError("Cannot convert {} to numpy array".format(
            type(tensor)))
    return tensor


def to_torch(ndarray):
    if type(ndarray).__module__ == 'numpy':
        return torch.from_numpy(ndarray)
    elif not torch.is_tensor(ndarray):
        raise ValueError("Cannot convert {} to torch tensor".format(
            type(ndarray)))
    return ndarray


def cleanexit():
    import sys
    import os
    try:
        sys.exit(0)
    except SystemExit:
        os._exit(0)

def load_model_wo_clip(model, state_dict):
    missing_keys, unexpected_keys = model.load_state_dict(state_dict, strict=False)
    assert len(unexpected_keys) == 0
    assert all([k.startswith('clip_model.') for k in missing_keys])

def freeze_joints(x, joints_to_freeze):
    # Freezes selected joint *rotations* as they appear in the first frame
    # x [bs, [root+n_joints], joint_dim(6), seqlen]
    frozen = x.detach().clone()
    frozen[:, joints_to_freeze, :, :] = frozen[:, joints_to_freeze, :, :1]
    return frozen